A New Mental Health Mobile App for Well-Being and Stress Reduction in Working Women: Randomized Controlled Trial

Coelhoso, Cássia Canha; Tobo, Patrícia Renovato; Lacerda, Sheylla Nadjane Batista; Lima, Alex Heitor; Barrichello, C.; Amaro, Edson; Kozasa, Elisa Harumi

doi:10.2196/14269

Cited by 64 publications

(66 citation statements)

References 70 publications

(81 reference statements)

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“…Indeed, having similar experiences during the pandemic, peer health care workers may be in a unique position to provide practical and emotional support. A systematic review showed that digital peer support interventions led to improved self-coping and adaptation [27]. Through candid sharing of experiences and validation of feelings, app-based peer support, embedded in a social influence nudge strategy, could complement existing in-person support and have a positive impact on frontline workers' psychological well-being [52].…”

Section: Principal Findingsmentioning

confidence: 99%

“…Individualized tailoring has been found to be an effective nudge in sustaining user engagement for digital mental health interventions [24][25][26]. In addition, provision of app-based resources yielded a positive outcome that alleviated chronic stress of working women [27]. Taken together, nudge theory has proved to be useful in effecting behavior change for mHealth interventions.…”

Section: Introductionmentioning

confidence: 98%

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Perceptions of Mobile Health Apps and Features to Support Psychosocial Well-being Among Frontline Health Care Workers Involved in the COVID-19 Pandemic Response: Qualitative Study

Yoon¹,

Goh²,

Nadarajan³

et al. 2021

J Med Internet Res

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Background Frontline health care workers are experiencing a myriad of physical and psychosocial challenges amid the COVID-19 pandemic. There is growing recognition that digital technologies have the potential to improve the well-being of frontline workers. However, there has been limited development of wellness interventions using mobile health (mHealth) technology. More importantly, little research has been conducted on how frontline workers perceive mHealth-based support to promote their well-being. Objective This study aimed to explore frontline workers’ experience of conventional psychological wellness programs and their perceptions of the usefulness of mHealth apps and features for promoting well-being. It also sought to identify factors that could potentially influence uptake and retention of an mHealth-based wellness program. Methods We conducted semistructured interviews using purposive sampling with frontline workers involved in the COVID-19 response. Various visual materials, collated from existing mHealth app features, were presented to facilitate discussion. Interviews were audio-recorded and transcribed verbatim. Thematic analysis based on grounded theory was undertaken. Themes were subsequently mapped to key nudge strategies—those commonly used for mHealth development—to assess participants’ preferences for particular features and their reasoning. Results A total of 42 frontline workers participated in 12 one-on-one interviews or focus group discussions. Frontline workers generally had a limited ability to identify their own psychological problems and liked the reminders functionality of the app to track their mood over time. A personalized goal-setting feature (ie, tailoring) and in-app resources were generally valued, while frequent coaching and messages (ie, framing) were seen as a distraction. The majority of participants desired a built-in chat function with a counselor (ie, guidance) for reasons of accessibility and protection of privacy. Very few participants appreciated a gamification function. Frontline workers commonly reported the need for ongoing social support and desired access to an in-app peer support community (ie, social influence). There were, however, concerns regarding potential risks from virtual peer interactions. Intrinsic motivational factors, mHealth app technicality, and tangible rewards were identified as critical for uptake and retention. Conclusions Our study highlights the potential of mHealth apps with relevant features to be used as wellness tools by frontline health care workers. Future work should focus on developing a nonintrusive and personalized mHealth app with in-app counseling, peer support to improve well-being, and tangible and extrinsic rewards to foster continued use.

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Section: Principal Findingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Perceptions of Mobile Health Apps and Features to Support Psychosocial Well-being Among Frontline Health Care Workers Involved in the COVID-19 Pandemic Response: Qualitative Study

Yoon¹,

Goh²,

Nadarajan³

et al. 2021

J Med Internet Res

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“…Although numerous studies have been conducted on MI apps [9,[14][15][16][17][18], few have used the algorithm of artificial neural networks (ANNs) in classifying MI, particularly with convolutional neural networks (CNNs). Traditionally, cutoff scores are often used in the classification of MI [19,20].…”

Section: App At Workplacementioning

confidence: 99%

“…Articles on MI apps have been published in the literature [14][15][16][17][18]43,44]. Over 45 articles were found by searching the keywords (app[title] and "mental health"[title]) in PubMed Central (PMC) on January 10, 2020.…”

Section: What This Knowledge Adds To What We Already Knowmentioning

confidence: 99%

An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study (Preprint)

Yan¹,

Chien²,

Yeh³

et al. 2020

Preprint

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BACKGROUND Background: Mental illness(MI) is common among those who work in healthcare settings. Whether MI is related to employees’ mental status at work has yet been known. An MI app is developed and proposed to help employees assess their mental status in the hope of detecting MI at an earlier stage. OBJECTIVE The aim of this study is to build a model using the convolutional neural networks(CNN) and fit statistics based on two aspects of measures and Outfit mean square errors(MNSQ) for automatic detection and classification of personal MI at the workplace using the emotional labor and mental health(ELMH) questionnaire so as to equip the staff the ability to assess and understand their own mental status with an app on their mobile device. METHODS We recruited 352 respiratory therapists (RT) working in Taiwan medical centers and regional hospitals to fill out the 44-item ELMH questionnaire in March 2019. The exploratory factor analysis(EFA), Rasch analysis, and CNN were used as unsupervised and supervised learnings for (1) dividing RTs into four classes(i.e., MI, false MI, health, and false health) and (2) building an ELMH predictive model to estimate 108 parameters of the CMM model. We calculated the prediction accuracy rate and created an app for classifying MI for RTs at the workplace as an online assessment. RESULTS We observed that (1) eight domains in ELMH were retained by EFA; (2) four types of mental health (n = 6, 63, 265, and 18 located in four quadrants) were classified by using Rasch analysis; (3) the 44-item model yields a higher accuracy rate (0.92); and (4) an MI app available for RTs predicting MI was successfully developed and demonstrated in this study. CONCLUSIONS The 44-item model with the 108 parameters estimated by using CNN for improving the accuracy of mental health for RTs. An MI app developed for helping RTs self-detect work-related MI at an early stage should be made more available and viable in the future. CLINICALTRIAL Not available

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“…It was evaluated for its efficacy at promoting stress management and well-being among working women. The results showed that the Flourish app was effective in reducing employees’ stress and at improving well-being [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

The Integration of Meditation and Positive Psychology Practices to Relieve Stress in Women Workers (Flourish): Effects in Two Pilot Studies

Santos

Lacerda

Coelhoso

et al. 2021

Behavioral Sciences

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Meditation and positive psychology have been reported as promising approaches to deal with stress. This exploratory pilot study aims to evaluate the efficacy of meditation and positive psychology-based intervention on stress. Two experiments are reported; in the first one, 29 healthcare professionals were included (intervention = 14; control = 15), and the second one included 57 women managers (intervention = 27; control = 30). All participants were evaluated before and after eight weeks for levels of stress, anxiety, depression, and psychiatric symptoms. In Experiment 1, intervention group participants showed a reduction in stress levels and anxiety symptoms, and in Experiment 2, participants in the intervention group showed a decrease in stress, psychiatric symptoms, and sleep quality, when compared to the control group. A multi-component intervention based on positive psychology and meditation practices was effective at reducing stress as well as improving related stress parameters such as anxiety, psychiatric symptoms, and sleep quality.

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A New Mental Health Mobile App for Well-Being and Stress Reduction in Working Women: Randomized Controlled Trial

Cited by 64 publications

References 70 publications

Perceptions of Mobile Health Apps and Features to Support Psychosocial Well-being Among Frontline Health Care Workers Involved in the COVID-19 Pandemic Response: Qualitative Study

Perceptions of Mobile Health Apps and Features to Support Psychosocial Well-being Among Frontline Health Care Workers Involved in the COVID-19 Pandemic Response: Qualitative Study

An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study (Preprint)

The Integration of Meditation and Positive Psychology Practices to Relieve Stress in Women Workers (Flourish): Effects in Two Pilot Studies

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